Pub Date : 2018-07-01DOI: 10.1109/ICCI-CC.2018.8482069
Xuanguang Ren, Han Pan, Zhongliang Jing, Lei Gao
Cognitive image processing is an important part of cognitive informatics. High quality images are crucial for cognitive image processing, especially in small object recognition and image segmentation. Multi-images restoration provides an alternative approach for these problems. For example, with image denoising and image deblurring, the raw images can be better provided to improve the result of cognitive image processing. The improvement of imaging device's sampling rate provides a clue to design a common approach for multi-images restoration. This paper concerns with a mixed-regularization approach for solving multi-images (MRMI) restoration problems. The MRMI algorithm generalizes the original total variation (TV) based algorithm by fusing multiple noisy images to maximize the useful information restored from the degraded images. The proposed approach combines $ell_{1}$ regularizer and $mathbf{TV}_{p}$ regularizer to restore a latent image, which operates on two different domains, i.e., pixel and gradient. This mixed-regularization method can simultaneously exploit the sparsity of natural signal. The resulting problem is solved by the adaptation of generalized accelerated proximal gradient (GAPG) method. The effectiveness of our approach is validated in the context of multi-images denoising, deblurring and inpainting. Compared with some iterative shrinkage-thresholding algorithms, the experimental results indicates that our approach can restore a better image.
{"title":"Multi-Images Restoration Method with a Mixed-Regularization Approach for Cognitive Informatics","authors":"Xuanguang Ren, Han Pan, Zhongliang Jing, Lei Gao","doi":"10.1109/ICCI-CC.2018.8482069","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482069","url":null,"abstract":"Cognitive image processing is an important part of cognitive informatics. High quality images are crucial for cognitive image processing, especially in small object recognition and image segmentation. Multi-images restoration provides an alternative approach for these problems. For example, with image denoising and image deblurring, the raw images can be better provided to improve the result of cognitive image processing. The improvement of imaging device's sampling rate provides a clue to design a common approach for multi-images restoration. This paper concerns with a mixed-regularization approach for solving multi-images (MRMI) restoration problems. The MRMI algorithm generalizes the original total variation (TV) based algorithm by fusing multiple noisy images to maximize the useful information restored from the degraded images. The proposed approach combines $ell_{1}$ regularizer and $mathbf{TV}_{p}$ regularizer to restore a latent image, which operates on two different domains, i.e., pixel and gradient. This mixed-regularization method can simultaneously exploit the sparsity of natural signal. The resulting problem is solved by the adaptation of generalized accelerated proximal gradient (GAPG) method. The effectiveness of our approach is validated in the context of multi-images denoising, deblurring and inpainting. Compared with some iterative shrinkage-thresholding algorithms, the experimental results indicates that our approach can restore a better image.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115227250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes the application of a search system for helping users find the most relevant answers to their questions from a set of documents. The system is developed based on a new algorithm for Natural Language Understanding (NLU) called Calibrated Quantum Mesh (CQM). CQM finds the right answers instead of documents. It also has the potential to resolve confusing and ambiguous cases by mimicking the way a human brain functions. The method has been evaluated on a set of queries provided by users. The relevant answers given by the Coseer search system have been judged by three human judges as well as compared to the answers given by a reliable answering system called AskCFPB. Coseer performed better in 57.0% of cases, and worse in 16.5% cases, while the results were comparable to AskCFPB in 26.6% of cases. The usefulness of a cognitive computing system over a Microsoft-powered key-word based search system is discussed. This is a small step toward enabling artificial intelligence to interact with users in a natural manner like in an intelligent chatbot.
{"title":"Cognitive Natural Language Search Using Calibrated Quantum Mesh","authors":"Rucha Kulkarni, Harshad Kulkarni, Kalpesh Balar, Praful Krishna","doi":"10.1109/ICCI-CC.2018.8482018","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482018","url":null,"abstract":"This paper describes the application of a search system for helping users find the most relevant answers to their questions from a set of documents. The system is developed based on a new algorithm for Natural Language Understanding (NLU) called Calibrated Quantum Mesh (CQM). CQM finds the right answers instead of documents. It also has the potential to resolve confusing and ambiguous cases by mimicking the way a human brain functions. The method has been evaluated on a set of queries provided by users. The relevant answers given by the Coseer search system have been judged by three human judges as well as compared to the answers given by a reliable answering system called AskCFPB. Coseer performed better in 57.0% of cases, and worse in 16.5% cases, while the results were comparable to AskCFPB in 26.6% of cases. The usefulness of a cognitive computing system over a Microsoft-powered key-word based search system is discussed. This is a small step toward enabling artificial intelligence to interact with users in a natural manner like in an intelligent chatbot.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123041536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/ICCI-CC.2018.8482072
Zhenhao Wang, Yi Wang, Bing-Qian Liu, Dong-ni Pan, Xuebing Li
Due to the difficulty of trigger automatic emotion processing, the automatic emotion regulation of anger and fear is unclear and to explore. Using a priming procedure and ERP analysis, the current study investigated the time course of automatic emotion regulation and difference between anger and fear. 46 participants were required to finish a word matching task to activate the processing of emotion regulation unconsciously, then to complete angry and fearful facial expression identify task. Subjective self-reports and ERP results verified that: (1) priming procedure could effectively evoke automatic emotion regulation; (2) N170, EPN and LPP components analysis supported that the automatic emotion regulation started at the early stage, continued to the middle stage, and dissolved at the late stage of emotion processing; (3) anger and fear are two difference emotions with same automatic emotion regulation mechanism but distinct emotion processing.
{"title":"Neurological Foundations of the Brain in the Automatic Emotion Regulation of Anger and Fear","authors":"Zhenhao Wang, Yi Wang, Bing-Qian Liu, Dong-ni Pan, Xuebing Li","doi":"10.1109/ICCI-CC.2018.8482072","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482072","url":null,"abstract":"Due to the difficulty of trigger automatic emotion processing, the automatic emotion regulation of anger and fear is unclear and to explore. Using a priming procedure and ERP analysis, the current study investigated the time course of automatic emotion regulation and difference between anger and fear. 46 participants were required to finish a word matching task to activate the processing of emotion regulation unconsciously, then to complete angry and fearful facial expression identify task. Subjective self-reports and ERP results verified that: (1) priming procedure could effectively evoke automatic emotion regulation; (2) N170, EPN and LPP components analysis supported that the automatic emotion regulation started at the early stage, continued to the middle stage, and dissolved at the late stage of emotion processing; (3) anger and fear are two difference emotions with same automatic emotion regulation mechanism but distinct emotion processing.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114070676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/ICCI-CC.2018.8482048
Zhong Ma, Zhuping Wang
In this paper, we proposed a Bag-of-Words image representation method in-spired by visual attention by applying computational visual attention technology to the representation of images, thus to boost the performance of the object discovery. First, a computational visual attention model was built on the real eye tracking data. With this attention model, we can find the most salient regions from the image, and then representing the image by emphasizes the visual words in these regions. Thus, we can get a Bag of Words image representation with more discriminative power, reducing the confusion intruded by the background on the images. Beyond discovering the objects from the images, with the guidance of the visual attention model, we are also able to find their locations. The experiment was carried out to verify the effectiveness of the proposed method. The experimental results showed that the proposed method improves the performance of the object discovery algorithm.
本文提出了一种受视觉注意启发的词袋图像表示方法，将计算视觉注意技术应用到图像表示中，从而提高了目标发现的性能。首先，基于真实眼动追踪数据建立计算视觉注意模型;利用该注意模型，我们可以从图像中找到最突出的区域，然后通过强调这些区域中的视觉词来表示图像。这样，我们可以得到一个判别能力更强的Bag of Words图像表示，减少了背景对图像的干扰。除了从图像中发现物体之外，在视觉注意模型的指导下，我们还可以找到它们的位置。通过实验验证了该方法的有效性。实验结果表明，该方法提高了目标发现算法的性能。
{"title":"Visual Cognitive Attention Based Bag-of-Words Image Representation for Object Discovery","authors":"Zhong Ma, Zhuping Wang","doi":"10.1109/ICCI-CC.2018.8482048","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482048","url":null,"abstract":"In this paper, we proposed a Bag-of-Words image representation method in-spired by visual attention by applying computational visual attention technology to the representation of images, thus to boost the performance of the object discovery. First, a computational visual attention model was built on the real eye tracking data. With this attention model, we can find the most salient regions from the image, and then representing the image by emphasizes the visual words in these regions. Thus, we can get a Bag of Words image representation with more discriminative power, reducing the confusion intruded by the background on the images. Beyond discovering the objects from the images, with the guidance of the visual attention model, we are also able to find their locations. The experiment was carried out to verify the effectiveness of the proposed method. The experimental results showed that the proposed method improves the performance of the object discovery algorithm.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"74 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114013425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/ICCI-CC.2018.8482074
B. Widrow
Hebbian learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, communication systems, pattern recognition, and artificial neural networks. These learning paradigms are very different. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and, as such, LMS can be used in a natural way to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm, Hebbian-LMS. This algorithm has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is, how does learning take place in living neural networks? “Nature's little secret,” the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.
{"title":"Nature's Learning Rule: The Hebbian-LMS Algorithm","authors":"B. Widrow","doi":"10.1109/ICCI-CC.2018.8482074","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482074","url":null,"abstract":"Hebbian learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, communication systems, pattern recognition, and artificial neural networks. These learning paradigms are very different. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and, as such, LMS can be used in a natural way to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm, Hebbian-LMS. This algorithm has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is, how does learning take place in living neural networks? “Nature's little secret,” the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114725879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The task of providing Natural Language Interface (NLI) to any domain specific knowledge base is much demanding despite (potentially) favorable factors like low volume of vocabulary, unambiguous and precise meaning of words, less number of relations among the entities, etc. The simplification of this task has been proposed and presented in this research. The authors have made a successful effort to develop an NLI system to answer the user's simple queries (in English) about the properties of chemical elements and their grouping in the Periodic Table. Adding to the ease, the user is not required to know anything about the structure of the knowledge base of the elements, since the software is implemented (using Logic Programming constructs) in Prolog wherein program and data are treated indistinguishably. Firstly, the system accepts a query and subsequently, it can analyze and understand the query, if the query contains all words within the domain specific vocabulary. Finally, it efficiently searches the knowledge base to answer the query, by reducing search space using artificial intelligence techniques (like symbolic manipulation). If the query is not understood by the system, it reports to the user the words not available in the knowledge base and the particular relations among the entities which could not be set. The knowledge base (~150 KB) contains the properties of chemical elements, their arrangement in Periodic Table and the inter-relationships among these properties. In a nutshell, the research suggests that to develop an NLI to a domain specific knowledge base, it is better to develop a parser capable of handling the entities and their interrelationships as understood in the domain; hence, only little is to be coded for the various grammars, languages, transition networks, etc.
{"title":"Natural Language Interfaces to Domain Specific Knowledge Bases: An Illustration for Querying Elements of the Periodic Table","authors":"Mukesh Kumar Rohil, Rohan Kumar Rohil, Divyesakshi Rohil, Anurag Runthala","doi":"10.1109/ICCI-CC.2018.8482023","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482023","url":null,"abstract":"The task of providing Natural Language Interface (NLI) to any domain specific knowledge base is much demanding despite (potentially) favorable factors like low volume of vocabulary, unambiguous and precise meaning of words, less number of relations among the entities, etc. The simplification of this task has been proposed and presented in this research. The authors have made a successful effort to develop an NLI system to answer the user's simple queries (in English) about the properties of chemical elements and their grouping in the Periodic Table. Adding to the ease, the user is not required to know anything about the structure of the knowledge base of the elements, since the software is implemented (using Logic Programming constructs) in Prolog wherein program and data are treated indistinguishably. Firstly, the system accepts a query and subsequently, it can analyze and understand the query, if the query contains all words within the domain specific vocabulary. Finally, it efficiently searches the knowledge base to answer the query, by reducing search space using artificial intelligence techniques (like symbolic manipulation). If the query is not understood by the system, it reports to the user the words not available in the knowledge base and the particular relations among the entities which could not be set. The knowledge base (~150 KB) contains the properties of chemical elements, their arrangement in Periodic Table and the inter-relationships among these properties. In a nutshell, the research suggests that to develop an NLI to a domain specific knowledge base, it is better to develop a parser capable of handling the entities and their interrelationships as understood in the domain; hence, only little is to be coded for the various grammars, languages, transition networks, etc.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124170401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/ICCI-CC.2018.8482020
J. Gai, Haochen Du, Qi Liu
The recently proposed Modulated Wideband Converter (MWC) sampling method, for sparse wideband signals, can implement sampling without distortion at a rate lower than that prescribed by Nyquist, which alleviates the pressure from high sampling rate. However, the existing recovery algorithm of MWC is far from satisfactory in terms of recovery performance. In this paper, a high-performance recovery algorithm for support is proposed, combining null space and random dimensionality reduction methods. The proposed algorithm firstly uses random transform to convert the sampling equation to a multiple-measurement-vector problem with low dimension, and then utilizes the orthogonal relation between null space and the sampling matrix to judge the support set. Finally the accurate reconstruction is performed by pseudo-inverse operation. The experimental results show that this algorithm can significantly improve the success rate of recovery compared with the traditional OMPMMV algorithm.
{"title":"Support Recovery for MWC Based on Random Reduction and Null Space","authors":"J. Gai, Haochen Du, Qi Liu","doi":"10.1109/ICCI-CC.2018.8482020","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482020","url":null,"abstract":"The recently proposed Modulated Wideband Converter (MWC) sampling method, for sparse wideband signals, can implement sampling without distortion at a rate lower than that prescribed by Nyquist, which alleviates the pressure from high sampling rate. However, the existing recovery algorithm of MWC is far from satisfactory in terms of recovery performance. In this paper, a high-performance recovery algorithm for support is proposed, combining null space and random dimensionality reduction methods. The proposed algorithm firstly uses random transform to convert the sampling equation to a multiple-measurement-vector problem with low dimension, and then utilizes the orthogonal relation between null space and the sampling matrix to judge the support set. Finally the accurate reconstruction is performed by pseudo-inverse operation. The experimental results show that this algorithm can significantly improve the success rate of recovery compared with the traditional OMPMMV algorithm.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128110062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/ICCI-CC.2018.8482070
S. Nobukawa, H. Nishimura, Teruya Yamanishi
In the cerebral cortex, the distribution of excitatory post-synaptic potential exhibits log-normal distribution. Recently, it has been reported that this distribution generates a spontaneous activity. Moreover, this distribution may have useful effect in enhancing abilities of associative memory recall and can induce burst spiking to play a crucial role in memory consolidation. The weak synaptic networks in this log-normal distribution exhibit random network characteristics, while the strong synaptic networks have small-world characteristics. The concern with the functionality of fluctuation of neural activity and duality of synaptic connectivity has been brought to public attention. Therefore, in this study, to determine the relationship between the complexity of spontaneous activity and duality of synaptic connectivity, we introduced a spiking neural network with the duality of synaptic connectivity. Subsequently, we conducted multiscale entropy analysis for spontaneous activity and clustering analysis of emergent spiking pattern. The results revealed that in case wherein strong synaptic connections exhibit intermediate characteristic of small world network, specific spiking patterns arise among the spatio-temporal irregular spiking activity. Additionally, multi-scale entropy profile of the spiking activity exhibits a unimodal maximum peak at a slow temporal scale corresponding to the profile of the actual brain activity.
{"title":"Emergent Patterns and Spontaneous Activity in Spiking Neural Networks with Dual Complex Network Structure","authors":"S. Nobukawa, H. Nishimura, Teruya Yamanishi","doi":"10.1109/ICCI-CC.2018.8482070","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482070","url":null,"abstract":"In the cerebral cortex, the distribution of excitatory post-synaptic potential exhibits log-normal distribution. Recently, it has been reported that this distribution generates a spontaneous activity. Moreover, this distribution may have useful effect in enhancing abilities of associative memory recall and can induce burst spiking to play a crucial role in memory consolidation. The weak synaptic networks in this log-normal distribution exhibit random network characteristics, while the strong synaptic networks have small-world characteristics. The concern with the functionality of fluctuation of neural activity and duality of synaptic connectivity has been brought to public attention. Therefore, in this study, to determine the relationship between the complexity of spontaneous activity and duality of synaptic connectivity, we introduced a spiking neural network with the duality of synaptic connectivity. Subsequently, we conducted multiscale entropy analysis for spontaneous activity and clustering analysis of emergent spiking pattern. The results revealed that in case wherein strong synaptic connections exhibit intermediate characteristic of small world network, specific spiking patterns arise among the spatio-temporal irregular spiking activity. Additionally, multi-scale entropy profile of the spiking activity exhibits a unimodal maximum peak at a slow temporal scale corresponding to the profile of the actual brain activity.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127914638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/ICCI-CC.2018.8482096
Yuefeng Sun, Zhengnan Gao, Shubo Hu, Hui Sun, Anlong Su, Shunjiang Wang, Kai Gao, Wei-chun Ge
The popularization and application of PMU measurement devices in power system provides real-time data monitoring tools for power grid operators. However, the measuring time interval of measuring devices is extremely short. The processing and analysis of the big data generated by measuring devices presents new requirements for the power system, and brings new challenges to the operators. In this paper, the method of parameter estimation of transmission line using cloud computing based on distributed intelligence is studied in depth. An efficient solution aim at processing the big data is given. The k-means clustering algorithm is used to fit the actual situation of the transmission line parameters under the temperature and humidity micro-meteorology. A new way of the mass data application is provided in this paper. The experimental example proves that the cloud computing model based on distributed intelligence can greatly improve the computational efficiency and save the computing time. In addition, the parameters of the transmission line in micro-meteorology conform to the actual operation of the power grid, and early warning can be provided to operators when the real-time operating parameters change suddenly.
{"title":"A Method of Estimating Transmission Line Parameters Using Cloud Computing Based on Distributed Intelligence","authors":"Yuefeng Sun, Zhengnan Gao, Shubo Hu, Hui Sun, Anlong Su, Shunjiang Wang, Kai Gao, Wei-chun Ge","doi":"10.1109/ICCI-CC.2018.8482096","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482096","url":null,"abstract":"The popularization and application of PMU measurement devices in power system provides real-time data monitoring tools for power grid operators. However, the measuring time interval of measuring devices is extremely short. The processing and analysis of the big data generated by measuring devices presents new requirements for the power system, and brings new challenges to the operators. In this paper, the method of parameter estimation of transmission line using cloud computing based on distributed intelligence is studied in depth. An efficient solution aim at processing the big data is given. The k-means clustering algorithm is used to fit the actual situation of the transmission line parameters under the temperature and humidity micro-meteorology. A new way of the mass data application is provided in this paper. The experimental example proves that the cloud computing model based on distributed intelligence can greatly improve the computational efficiency and save the computing time. In addition, the parameters of the transmission line in micro-meteorology conform to the actual operation of the power grid, and early warning can be provided to operators when the real-time operating parameters change suddenly.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115935809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-07-01DOI: 10.1109/ICCI-CC.2018.8482042
Geliang Tian, Yue Liu
The performance of classification is one of the most key issues in brain computer interface (BCI) system. This paper proposes a classification method of two-class motor imagery electroencephalogram (EEG) signals based on convolutional neural networks (CNN), in which EEG signals from C3, C4 and Cz electrodes of publicly available BCI competition IV dataset 2b were used to test the performance of CNN. We investigate CNN with a form of input from short time Fourier transform (STFT) combining time, frequency and location information. Fisher discriminant analysis-type F-score based on band pass (BP) feature and power spectra density (PSD) feature are employed respectively to select the subject-optimal frequency bands. In the experiments, typical frequency bands related to motor imagery EEG signals, subject-optimal frequency bands and Extension Frequency Bands are employed respectively as the frequency range of the input image of CNN. The better classification performance of Extension Frequency Bands show that CNN can extract optimal feature from frequency information automatically. The classification result also demonstrates that the proposed approach is more competitive in prediction of left/right hand motor imagery task compared with other state-of-art approaches.
{"title":"Study on Classification of Left-Right Hands Motor Imagery EEG Signals Based on CNN","authors":"Geliang Tian, Yue Liu","doi":"10.1109/ICCI-CC.2018.8482042","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482042","url":null,"abstract":"The performance of classification is one of the most key issues in brain computer interface (BCI) system. This paper proposes a classification method of two-class motor imagery electroencephalogram (EEG) signals based on convolutional neural networks (CNN), in which EEG signals from C3, C4 and Cz electrodes of publicly available BCI competition IV dataset 2b were used to test the performance of CNN. We investigate CNN with a form of input from short time Fourier transform (STFT) combining time, frequency and location information. Fisher discriminant analysis-type F-score based on band pass (BP) feature and power spectra density (PSD) feature are employed respectively to select the subject-optimal frequency bands. In the experiments, typical frequency bands related to motor imagery EEG signals, subject-optimal frequency bands and Extension Frequency Bands are employed respectively as the frequency range of the input image of CNN. The better classification performance of Extension Frequency Bands show that CNN can extract optimal feature from frequency information automatically. The classification result also demonstrates that the proposed approach is more competitive in prediction of left/right hand motor imagery task compared with other state-of-art approaches.","PeriodicalId":164976,"journal":{"name":"2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132147378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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